How clouds falter... and how to straighten them

When we talk about cloud computing clouds ‘breaking’, we mean it in the sense of the IT in question failing to achieve its specified level of performance and availability, but Machine Learning tools are coming forward to automate our engineers’ multifarious remediation responsibilities.

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Clouds don’t actually break. That core truism comes from the fact that clouds are, by definition, virtualised resources of compute, storage and analytics, all created as an instance inside a server unit, somewhere inside a datacentre.

Usually backed up for disaster recovery purposes with appropriate levels of redundancy, the only way for a cloud to physically break would be for someone to take a sledge hammer to a server rack and wreak havoc upon the chunks of metal and silicon therein. Even then, there ought to be backup.

What cloud breakage really means

When we do talk about clouds breaking, we mean it in the sense of the entire IT instance (or smaller cloud component) in question failing to achieve its specified level of performance. This happens when its functions become log-jammed in some way so that its core level of availability fails to serve the users who access the services it feeds.

In real world operational terms, there are many and various reasons for cloud service degradation or failure. Whatever the reason for cloud breakage, it is actually more commonly just known as downtime.

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